Systems and methods for end-to-end article management

ABSTRACT

Systems and methods are described for managing articles. The systems and methods described herein may comprise an example method for manufacturing an article. The systems and methods provides an end-to-end manufacturing value chain as a closed system and feedback loop.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/684,456 filed Nov. 14, 2019, which claims priority to and the benefitof U.S. Patent Application Ser. No. 62/768,506 filed Nov. 16, 2018, eachof which is hereby incorporated by reference in its entirety.

BACKGROUND

Traditional fabric manufacturing processes may take a long time fromconception of an article of clothing to production of the article ofclothing. However, changes in consumer clothing trends may occurquickly. Additionally, changes in manufacturing parameters, such asresources available, may affect whether a particular article of clothingmay be created. By the time a manufacturing process has changed whicharticles of clothing are manufactured to adapt to a new trend andaccount for the available resources, another new clothing trend mayemerge. What is needed is a fabric manufacturing process that may moreefficiently adapt to changing trends and real-time manufacturingparameters.

SUMMARY

The present disclosure relates, in one or more aspects, to an end-to-endprocess for article management. Such articles may comprise clothing,apparel, accessories, components comprising fabrics, and the like. Thepresent disclosure relates, in one or more aspects, to producing anarticle within tolerance of the design for such article. Often, inconventional processes, the steps of article management are discrete anddisjunctive, with transitions between steps of the process introducingerror or discrepancies from intended design. The end-to-end processes ofthe present disclosure may minimize such discrepancies and mayfacilitate the production of articles such as apparel to within tighttolerances of the intended design. In particular, color of a finishedarticle may be within a predetermined tolerance of the designed color.Alternatively or additionally, the methods and systems of the presentdisclosure may facilitate dynamic pricing, dynamic lead time, dynamicbatching, dynamic delivery, and may provide a personalized or customizedprocess for customers.

Conventional methods are locked into long forecast-driven supply chains.The present disclosure provides a demand driven apparel manufacturingprocess by moving process steps such as coloration closer to theconsumer.

Systems and methods are described for managing material such as fabricmanufacturing. The systems and methods described herein may comprise anexample method for manufacturing an article. The example method maycomprise receiving consumer data comprising at least biometricinformation associated with one or more consumers. The example methodmay comprise receiving design inputs indicative of a design of anarticle, wherein the design of the article is based on the consumerdata. The example method may comprise causing output of interactivecontent to a user interface associated with the one or more consumers,wherein the interactive content comprises at least a representation ofthe design of the article. The example method may comprise outputtingmanufacturing data indicative of instructions associated withmanufacture of the article, wherein the instructions are based on thedesign of the article. These and other fabric manufacturing managementmethods and systems are described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings show generally, by way of example, but not by wayof limitation, various examples discussed in the present disclosure. Inthe drawings:

FIGS. 1A-1E show an example diagram of a manufacturing process.

FIG. 2 shows an example diagram of a design process.

FIG. 3 is a flow diagram of an example method.

FIG. 4 is a flow diagram of an example method.

FIG. 5 shows an example diagram of nesting.

FIG. 6 shows an example diagram of an article management process.

FIG. 7A is example data based on a treatment of the present disclosure.

FIG. 7B illustrates a formulation of an example treatment.

FIG. 7C illustrates a formulation of an example treatment.

FIG. 7D is a flow diagram of an example method.

FIG. 8 is a flow diagram of an example method.

FIG. 9A is a flow diagram of an example method.

FIGS. 9B-9E illustrate example traceability mechanisms.

FIG. 10 is a process diagram.

FIG. 11 illustrates an example system.

FIG. 12 illustrates an example system.

FIG. 13 illustrates an example system.

FIG. 14 illustrates an example process flow.

FIG. 15 is a flow diagram of an example method.

FIGS. 16 -17 illustrate an example system.

DETAILED DESCRIPTION

Systems and methods are described for managing articles such asclothing/apparel including but not limited to, shirts, pants, shorts,footwear, and bags, for example only,. The systems and/or methods maycomprise end-to-end article management such as manufacturing. Thesystems and/or methods may comprise every manufacturing aspect from adesign of an article of clothing to delivery of the article of clothingto a customer. The systems and/or methods may capture information fromone or more steps of a management or manufacturing process to influenceother steps of the management or manufacturing process. Reference may bemade herein to fabric or textiles as an illustration. However,application to a broader range of materials is contemplated and thusshould not be limited to such illustrative terms.

The systems and/or methods described herein may comprise one or moretools, units, or plants for managing articles such as apparel orclothing from fabric to customer. The systems and/or methods describedherein may comprise one or more clothing manufacturing plants. Thesystems and/or methods described herein may comprise one or morecomputing devices associated with the one or more clothing manufacturingplants associated with one or more respective clothing manufacturers.The systems and/or methods described herein may comprise one or morecloud computing environments associated with the one or more clothingmanufacturing plants. The systems and/or methods described herein maycomprise one or more client devices, such as laptops, desktops, smartphones, wearable devices, tablets, etc. The one or more client devicesmay be in communication with the one or more computing devices and/orthe one or more cloud computing environments via a network. The one ormore client devices may comprise one or more applications executing onthe one or more client devices.

The systems and/or methods described herein may comprise a businessprocess. The business process may comprise a process for creation of ajob file (e.g., 110 in FIG. 1A). The job file may comprise instructionsfor authoring tools, instructions for digital asset management, and/orinstructions for patterns and/or markers. The authoring tools maycomprise 2-dimensional design tools, 3-dimensional polygonal designtools, and/or 3-dimensional parametric design tools. Digital assetmanagement may comprise information about digital materials, graphics,images, 3-dimensional assets, color profiles, fit blocks, design librarymanagement, material development, line planning, bill of materials,material testing, vendor collaboration, and/or financial planning.Digital asset management, as described herein, may comprise or may bebased on data regarding materials physical properties, material spectralreflectance and refractance properties, materials performanceproperties, materials provenance and related resource consumption, batchserialization, and the like. The patterns and/or markers may compriseinformation about patterns, reference points, cut data, grading, graphicimages, colors, cut plans, job status management, and/or materialutilization. The job file may comprise options to be selected by a user.The job file may comprise options selected by the user. The job file maycomprise options selected without intervention by the user.

The job file may be created by one or more computing devices (“job filecreator”). The job file creator may be in communication with one or morecomputing devices configured to gather real-time and/or near real-timemanufacturing and/or consumer data (“data gathers”) via a network. Thejob file and/or parameters available associated with the job file may beinfluenced by real-time data received from the data gathers. The jobfile creator may be in communication with one or more computing devicesconfigured to cause execution of one or more manufacturing steps(“controllers”) via a network. The job file creator may provide the jobfile to the controllers. The controllers may cause execution of the oneor more manufacturing steps in accordance with the job file.

The systems and/or methods described herein may comprise a feedback loopfor designers. Biometric data and/or consumer data may be captured andtrends may be identified. The options available to designers on theirdesign tools may be influenced by the captured biometric and/or consumerdata. The designers may design clothing based on the options availableon their design tools. Biometric and/or consumer data associated withthe designed clothing may be captured and trends may be identified, thusrestarting the feedback loop.

The systems and/or methods described herein may use nesting toefficiently use materials that need to be cut. Nesting involvesarranging patterns that are cut from materials such that waste from thecutting is lessened. Nesting may involve arranging components that havesimilar or the same colors and/or patterns at borders such that two suchborders of two components are adjacent to each other. Nesting mayinvolve using color overlap between two or more components. Nesting mayinvolve dynamically batching orders.

The systems and/or methods described herein may comprise a foampretreatment process. The foam pretreatment process may replace atraditional dipping process. The foam pretreatment process may reducewater. The foam pretreatment process may reduce energy. The foampretreatment process may reduce the use of chemicals. The foampretreatment process may achieve deeper and/or richer colors. The foampretreatment process may dry easier than the traditional dippingprocess.

The systems and/or methods described herein may comprise a plasmapre-cleaning/activation process. The atmospheric plasmapre-cleaning/activation process may comprise corona plasma. Theatmospheric plasma pre-cleaning/activation process may be used to cleanfabric and/or material and increase the surface roughness offabric/fibers and/or material to improve adhesion properties. Theatmospheric plasma pre-cleaning/activation process may be used tovaporize remove (decompose) contaminants (e.g., oils, waxes, etc.) fromfabric and/or material. The atmospheric plasma pre-cleaning/activationprocess may clean fabric and/or material after and/or before the foampretreatment and/or pad process. The atmospheric plasmapre-cleaning/activation process may activate fabric and/or material. Theplasma pre-cleaning/activation process may achieve deeper and/or moresaturated colors on fabric and/or material, while using less dyes and/orchemicals. The plasma pre-cleaning/activation process may be waterlessand occurs at environment temperature. The plasmapre-cleaning/activation process can be applied by different carriergases such as air, oxygen, nitrogen, helium, argon, hydrocarbon-basedgases, fluorocarbon-based gases and/or mixture of different gases. Eachgas provides different surface topography, chemistry and surface energyto the fabric and/or materials. Some grafting reaction(functionalization reactions) may take place between the fabric and/ormaterial and plasma carrier gas. Chemical composition of fabric and/ormaterial on the surface can be changed after plasma process. The systemsand/or methods described herein may comprise a colorization analyzeprocess. The colorization analyze process may compare an intended colorand an actual color. The colorization analyze process may determine theintended color from digital data, such as data from the job file. Thecolorization analyze process may determine the actual color usingcomputer vision.

The systems and/or methods described herein may comprise insertingand/or adding one or more sensors to a fabric operation, such as forin-line inspection of materials before, during, and/or after anoperation. The one or more sensors may comprise a spectrometer. The oneor more sensors may comprise an optical spectrometer. The one or moresensors may comprise a spectrophotometer. At one or more steps of amanufacturing process, the one or more sensors may be inspected toensure quality. The one or more sensors may be manually inspected by ahuman. The one or more sensors may be inspected by one or more computingdevices. Inspecting the one or more sensors by one or more computingdevices may comprise comparing an observed data set with an expecteddata set. Inspecting the one or more sensors by one or more computingdevices may comprise triggering an alert when a difference between theobserved data set and the expected data set is greater than apredetermined threshold. In an aspect, identification may be added to amaterial, such as bar codes, QR codes, invisible markers, etc. to enablereading or sensing that code with a reader device such as aspectrometer, for example.

The systems and/or methods described herein may comprise an observationprocess. The observation process may comprise observing patterns onfabric. The fabric may undergo one or more manufacturing steps. Theobservation process may comprise observing the patterns on the fabricafter the one or more manufacturing steps. The observation process maycomprise determining a delta between the observed pattern on the fabricbefore the one or more manufacturing steps and the observed pattern onthe fabric after the one or more manufacturing steps. The observationprocess may be performed by one or more computing devices (“observers”).The observers may provide the determined delta to one or more computingdevices in communication with a machine for cutting the fabric.

Digital Product Creation

Conventional processes for article creation comprise siloed and manualsteps/operations. The present disclosure enables consumers to customizethe products via ordering systems and may include the ability for userinputted data such as their measurements. Software may generateauto-patterns and the based on the tailoring rules that are beingestablished, the solution will pick an appropriate pattern. Suchsoftware may comprise custom selection of color or graphic(s), which maybe used in the auto-generation of a pattern or a selection of anexisting pattern. This pattern may then associated with the design billof materials comprising work instructions to a manufacturing site. Thesystems and methods may be integrated with back-end systems that enableon-demand manufacturing.

Made to Measure

Today, the front end consumer facing systems offer the capability tocustomize the products from the list of options. These options aremapped to the back-end manufacturing systems and are therefore limitedby the same. The ability to customize to suit a unique size is limited.Further, the options to personalize the product is also limited. Theoption to do this on demand is not existing. The present disclosureprovides the ability to customize the product, or add user inputtedinformation to the product. The systems and methods may dynamicallyconfigure the product to suit the individual's requirement and create apackage that is manufacture ready. The systems and methods may befurther extended to automate the bulk of the product creation.

Pre-Distortion of Image

In conventional textile processing, textile materials are processed in‘web’ form whereby mechanical forces and/or mechanical forces combinedwith heat cause distortion across the ‘web’. The implication is that animage printed on a digital printer can be controlled to an accuracywithin nanometers, but subsequent processing can result in distortionthat is not what the author intended. The present disclosure receivesinformation associated with the distortion of the raw material webthrough downstream processing and then map that distortion so that theimage applied to the web at the digital printing stage can be‘pre-distorted’ so that the final product matches the author'sintention. As an example, the present disclosure may address one or moreshortcomings of the conventional processes using feedbackloop/validation as show, for example, in FIG. 12.

Manipulation of materials in the manufacturing process (e.g.manipulating a printed upper material around a last) creates a bowingand skewing of imagery or patterns from the intended print. Inaccordance with the present disclosure, an example illustrated in atleast FIG. 9A, by including pre-distortion into the print-job file toaccount for downstream processes one can manufacture an end producttruer to the original form.

Material Categorization

Current industry practice is fragmented, paying no mind to substratesand their respective impact on multi-spectral color refractance,translucence, opacity, or the like. Additionally, substrate constructionhas material impact on the way a fabric drapes and/or flows in reallife. Additional input data such as one or more of whiteness Index, pH,degree of mercerization, refractance and reflectance index, thickness,compression, bending, roughness, friction, thermal properties,smoothness, softness, warmth, puckering, distortion, compositemeasurements thereof, ornatural observed performance history andvariance. In accordance with aspects of the present disclosure, bycollecting substrate characteristics and performance data (e.g.whiteness index, pH, and so forth) and integrating thesecharacteristics, one can digitally recreate critical design andperformance characteristics in a digital format, i.e. create a digitaltwin that we can produce an honest recreation of in real life.

Order Creation and Job Management

FIGS. 1A-1E show an example diagram of a management (e.g.,manufacturing) process. Although an example sequence is shown, it isunderstood that the various steps may be implemented in any order andmay be selectively implemented or not implemented. Feedback loops fromone or more downstream processes may be received and may be used toupdate one or more upstream processes. As an example, data collected atany one of the manufacturing steps may be shared upstream or downstreamin the end-to-end process and may be used to update other processes. Asa further example, all manufacturing steps may be implemented at asingle facility to provide complete end-to-end control. However, datashared between the steps may allow one or more processes to be atdifferent facilities without losing control or standardization.

At 100, a project request and/or order may be received. The projectrequest and/or order may be received at one or more computing devicesassociated with a clothing manufacturer. The project request and/ororder may be received at a cloud computing environment associated withthe clothing manufacturer. As used herein manufacture or manufacturermay refer to operations or entities associated with any portion ofmanagement of article production and delivery. The project requestand/or order may be received from a client device associated with acustomer.

In response to the project request and/or order being received, a job(e.g., order, project, etc.) file 110 may be created. The job file 110may comprise a graphic design file 112, grading information 114, aquantity/yardage request 116, kitting data 118, order data 120, apattern file 122, a substrate 124, finishing data 126, assembly data128, and/or tracking and/or routing data 130. The job file 110 maycomprise a bill of materials and/or serialization data. Otherinformation may be comprised in the job file 110 or may be referencedin/by the job file 110.

In response to the project request and/or order being receive, adetermination may be made if fabric associated with the project requestand/or order is already created at 102. The one or more computingdevices and/or the cloud computing environment associated with theclothing manufacturer may determine if fabric associated with theproject request and/or order is already created. If the fabric has notalready been created, then the process may move to 106. If the fabrichas already been created, then the process may move to 104.

At 104, a determination may be made if fabric associated with theproject request and/or order is in stock. The one or more computingdevices and/or the cloud computing environment associated with theclothing manufacturer may determine if fabric associated with theproject request and/or order is in stock. If the fabric is in stock,then the process may move to 108. If the fabric is not in stock, thenthe process may move to 106.

At 106, the fabric associated with the project request and/or order maybe ordered. The one or more computing devices and/or the cloud computingenvironment associated with the clothing manufacturer may order fabricfrom a fabric supplier. After the fabric associated with the projectrequest and/or order is ordered, a materials testing database 134 may beupdated and the process may move to 108.

At 108, a purchase order and/or a procedure to pay cycle associated withthe fabric associated with the project request and/or order may becaused to be processed. The one or more computing devices and/or thecloud computing environment associated with the clothing manufacturermay process the purchase order and/or the procedure to pay cycle. Theone or more computing devices and/or the cloud computing environmentassociated with the clothing manufacturer may cause another one or morecomputing devices to process the purchase order and/or the procedure topay cycle.

After the purchase order and/or the procedure to pay cycle associatedwith the fabric associated with the project request and/or order arecaused to be processed, inventory management may be performed at 132.The one or more computing devices and/or the cloud computing environmentassociated with the clothing manufacturer may perform inventorymanagement and/or cause inventory management to be performed. Performinginventory management may comprise updating an inventory to reflect thepurchase order and/or the procedure to pay cycle associated with thefabric associated with the project request and/or order. Performinginventory management may comprise using inventory information as part ofa fabric intake step at 138. Performing inventory management maycomprise updating inventory information based on the fabric intake stepat 138. Performing inventory management may comprise updating inventoryinformation based on a fabric pre-treatment step at 140.

The materials testing database 134 may be in communication with one ormore computing devices associated with a fabric supplier mill 136. Thematerials testing database 134 may cause an order for fabric to beplaced with the fabric supplier mill 136. The fabric supplier mill 136may cause fabric to be delivered to the clothing manufacturer as part ofthe fabric intake step at 138.

At 138, a material operator or manager, such as the clothingmanufacturer, may have or may receive material (e.g., fabric from thefabric supplier mill 136 as part of the fabric intake step at 138).Other materials may be used. After the fabric intake step, the processmay move to the fabric pre-treatment step at 140. It is understood thatis not to limit such a process to a garment manufacture, but is anon-limiting example. Other entities and operators may execute the sameor similar operations. In an aspect, operational capacity such asmanufacturing capacity for a particular product may be considered inorder to determine quoted lead time (e.g., in real time) and may enablesurge pricing/priority pricing.

After the fabric intake step at 138, a lab and/or visual inspection maybe performed at 142. The inspection may comprise an inspection by ahuman. The inspection may comprise an inspection using computer vision.The inspection may comprise an inspection of one or more sensors incommunication with the fabric. If the fabric fails the inspection, thenthe material testing database 134 may be updated (which, in turn, maycause the material testing database 134 to order more fabric from thefabric supplier mill 136). The results of the lab and/or visualinspection may be passed to a lab and/or visual inspection at 144.

The fabric pre-treatment step at 140 may comprise a foam pretreatmentprocess. The foam pretreatment process may replace a traditional dippingprocess. The foam pretreatment process may reduce water. The foampretreatment process may reduce energy. The foam pretreatment processmay reduce the use of chemicals. The foam pretreatment process mayachieve deeper and/or richer colors. The foam pretreatment process maydry easier than the traditional dipping process. Fabric that hasunderwent the foam pretreatment process may be used in a fabricconditioning step at 146.

After the fabric pre-treatment step at 140, the lab and/or visualinspection at 144 may be performed. The inspection may comprise aninspection by a human. The inspection may comprise computer vision,machine vision, and machine learning. The inspection may comprise aninspection of one or more sensors in communication with the fabric. Ifthe fabric fails the inspection, then the material testing database 134may be updated (which, in turn, may cause the material testing database134 to order more fabric from the fabric supplier mill 136). If thefabric fails inspection, it may also result in the generation of a neworder to backfill the failed material dependent on the type offailure/defect. The failure may result in a change to the quoted leadtime. The results of the lab and/or visual inspection may be passed toone or more computing devices involved in the fabric conditioning stepat 146.

The fabric conditioning step at 146 may comprise an atmospheric plasmapre-cleaning/activation process. The atmospheric plasmapre-cleaning/activation process may comprise corona plasma. Theatmospheric plasma pre-cleaning/activation process may be used to cleanfabric and/or material and increase the surface roughness offabric/fibers and/or material to improve adhesion properties. Theatmospheric plasma pre-cleaning/activation process may be used tovaporize remove (decompose) contaminants (e.g., oils, waxes, etc.) fromfabric and/or material. The atmospheric plasma pre-cleaning/activationprocess may clean fabric and/or material after and/or before the foampretreatment and/or pad process. The atmospheric plasmapre-cleaning/activation process may activate fabric and/or material. Theplasma pre-cleaning/activation process may achieve deeper and/or moresaturated colors on fabric and/or material, while using less dyes and/orchemicals. The plasma pre-cleaning/activation process may be waterlessand occurs at environment temperature. The plasmapre-cleaning/activation process can be applied by different carriergases such as air, oxygen, nitrogen, helium, argon, hydrocarbon-basedgases, fluorocarbon-based gases and/or mixture of different gases. Eachgas provides different surface topography, chemistry and surface energyto the fabric and/or materials. Some grafting reaction(functionalization reactions) may take place between the fabric and/ormaterial and plasma carrier gas. Chemical composition of fabric and/ormaterial on the surface can be changed after plasma process. Fabric thathas underwent the plasma pre-cleaning/activation process may be used ina printing step at 158.

After the fabric conditioning step at 146, a lab and/or visualinspection at 148 may be performed. The inspection may comprise aninspection by a human. The inspection may comprise an inspection usingcomputer vision. The inspection may comprise an inspection of one ormore sensors in communication with the fabric. If the fabric fails theinspection, then the material testing database 134 may be updated(which, in turn, may cause the material testing database 134 to ordermore fabric from the fabric supplier mill 136). The results of the laband/or visual inspection may be passed to one or more computing devicesinvolved in the printing step at 158.

At 150, the job file 110 may be used as part of a nested pattern step.One or more computing devices may use the job file 110 as part of thenested pattern step. The pattern file 122 of the job file 110 may beused as part of the nested pattern step. Other portions of the job file110 may also be used, such as the graphic design file 112, the gradinginformation 114, etc. The nested pattern step will be described in moredetail in reference to FIG. 3. After the nested pattern step, theprocess may move to 152. As described herein, nesting may be generatedor updated based on upstream or downstream information. Alternatively oradditionally, nesting may be updated based on information receivedrelating to downstream process or device performance. For example, if acutting process or machine, or if a material handling process ormachine/system is performing in a particular manner the nesting may beupdated based on such performance information.

At 152, a cut file may be generated. The cut file may be generated inresponse to the nested pattern step. One or more computing devices maygenerate the cut file. The cut file may comprise information for cuttingcomponents out of fabric. The cut file may be used in a cutting step at182. After the cut file is generated, the process may move to 154.Various files are referenced for illustration. It should be understoodthat several files or a single file may be used.

At 154, a color separation step may be performed. One or more computingdevices may perform the color separation step. The job file 110 may beused to perform the color separation step. After the color separationstep, the process may move to 156.

At 156, a raster image processing step may be performed. One or morecomputing devices may perform the raster image processing step. The jobfile 110 may be used to perform the raster image processing step. Afterthe raster image processing step, the process may move to 158.

At 158, the printing step may be performed. Although the term printingis used, it should be understood that drop on demand references genericselective processes that include selective deposition of materials anddigital printing, for example. The printing step may comprise causingcolor and/or graphics to be printed on fabric. One or more computingdevices may cause color and/or graphics to be printed on fabric. Aresult of the raster image processing step at 156 may be used toinfluence the printing of the color and/or the graphics on the fabric.The job file 110 may be used to influence the printing of the colorand/or the graphics on the fabric. Fabric that has underwent theprinting process may be used in a post-print dying step at 166.

After the printing step at 158, a lab and/or visual inspection at 160may be performed. The inspection may comprise an inspection by a human.The inspection may comprise an inspection using computer vision. Theinspection may comprise an inspection of one or more sensors incommunication with the fabric. The inspection may determine if and towhat extent there are differences between an expected color and a coloractually printed on fabric during the printing step at 158. The resultsof the lab and/or visual inspection may be passed to one or morecomputing devices associated with a color control/printer calibrationstep at 162. The one or more computing devices associated with the colorcontrol/printer calibration step at 162 may provide information to helpwith the inspection at 160. The results of the lab and/or visualinspection may be passed to one or more computing devices involved inthe post-print dying step at 166.

At 162, one or more computing devices associated with the colorcontrol/printer calibration step may determine and/or receiveinformation indicative of a discrepancy between an expected color and acolor actually printed on fabric at 158. The one or more computingdevices associated with the color control/printer calibration step maydetermine a new paint color to associate with the expected color. Theone or more computing devices associated with the color control/printercalibration step may determine that the new paint color needs more orless of a particular color, such as red, blue, and/or green, to becloser to the expected color. The one or more computing devicesassociated with the color control/printer calibration step maycommunicate with one or more computing devices associated with an updatelibrary of addressable colors step at 164.

At 164, the one or more computing devices associated with the updatelibrary of addressable colors step may update a library of addressablecolors based on information from the one or more computing devicesassociated with the color control/printer calibration step. The one ormore computing devices associated with the update library of addressablecolors step may assign the determined new paint color to the expectedcolor. The one or more computing devices associated with the updatelibrary of addressable colors step may cause a new project requestand/or order at 110 using the updated library of addressable colors tobe created.

At 166, the post-print drying step may be performed. The post-printdying step may comprise drying fabric. One or more computing devices maycause the fabric to be dried. Fabric that has underwent the post-printdying process may be used in a fixation/steaming step at 170.

After the post-print drying step at 166, a lab and/or visual inspectionat 168 may be performed. The inspection may comprise an inspection by ahuman. The inspection may comprise an inspection using computer vision.The inspection may comprise an inspection of one or more sensors incommunication with the fabric. If the fabric fails the inspection, thenthe material testing database 134 may be updated (which, in turn, maycause the material testing database 134 to order more fabric from thefabric supplier mill 136). The results of the lab and/or visualinspection may be passed to one or more computing devices involved inthe fixation/steaming step at 170.

At 170, the fixation/steaming step may be performed. Thefixation/steaming step may comprise steaming fabric that has beenprinted and/or dyed. One or more computing devices may cause the fabricto be steamed. Fabric that has underwent the fixation/steaming step maybe used in a post-print washing step at 174.

After the fixation/steaming step at 170, a lab and/or visual inspectionat 172 may be performed. The inspection may comprise an inspection by ahuman. The inspection may comprise an inspection using computer vision.The inspection may comprise an inspection of one or more sensors incommunication with the fabric. The results of the lab and/or visualinspection may be passed to one or more computing devices involved inthe post-print washing step at 174.

At 174, the post-print washing step may be performed. The post-printwashing step may comprise washing fabric that has been steamed and/orfixated. One or more computing device may cause the fabric to be washed.Fabric that has underwent the post-print washing step may be used in apost-print drying step at 178.

After the post-print step at 174, a lab and/or visual inspection at 176may be performed. The inspection may comprise an inspection by a human.The inspection may comprise an inspection using computer vision. Theinspection may comprise an inspection of one or more sensors incommunication with the fabric. The results of the lab and/or visualinspection may be passed to one or more computing devices involved inthe post-print drying step at 178.

At 178, the post-print drying step may be performed. The post-printdrying step may comprise drying fabric that has been washed. One or morecomputing device may cause the fabric to be dried. Fabric that hasunderwent the post-print drying step may be used in a cutting step at182.

After the post-print drying step at 178, a lab and/or visual inspectionat 180 may be performed. The inspection may comprise an inspection by ahuman. The inspection may comprise an inspection using computer vision.The inspection may comprise an inspection of one or more sensors incommunication with the fabric. If the fabric fails the inspection, thenthe material testing database 134 may be updated (which, in turn, maycause the material testing database 134 to order more fabric from thefabric supplier mill 136). The results of the lab and/or visualinspection may be passed to one or more computing devices involved inthe cutting step at 182.

At 182, the cutting step may be performed. The dried fabric may be cut.The dried fabric may be cut according to the cut file generated at 152.One or more computing devices may cause the fabric to be cut. Fabricthat has underwent the cutting step may be used in a batching step at184.

At 184, the batching step may be performed. The cut fabric may bebatched. One or more computing devices may cause the fabric to bebatched. Fabric that has underwent the batching step may be used in akitting step at 186.

At 186, the kitting step may be performed. The batched fabric may bekitted. One or more computing devices may cause the fabric to be kitted.Fabric that has underwent the kitting step may be used in an assemblystep at 188.

At 188, the assembly step may be performed. The kitted fabric may beassembled. One or more computing devices may cause the fabric to beassembled. Fabric that has underwent the assembly step may be shipped tocustomers.

Other steps and processes may be performed. Steps may be selectivelyperformed or not performed. Data may be shared between the processes andprocesses may be updated based on the shared data relating to theperformance of the upstream and/or downstream processes and equipment.

Design/Product Development

Current design and product development tools are not digitally linked toany real-life production methods. In accordance with the presentdisclosure, digital product creation may comprise manufacturing(printing) instructions created from the design platform. Colorationfeasibility will have feedback loop to inform design platform and definedesigner's choices for the product.

FIG. 2 shows an example diagram of a design process. At 200, consumerdata may be received (e.g., collected, etc.). The consumer data maycomprise biometric data. The consumer data may be collected from one ormore consumers. The consumer data may be collected from one or morewearable devices. The consumer data may be collected from one or moree-commerce websites. The consumer data may be collected from a feedbackloop. The consumer data may be collected from a repository.

At 202, a designer user interface may influenced by the consumer data.Colors and/or designs that are options in design tools may be influencedby the consumer data. Colors and/or designs that are options in designtools may be influenced by business reasons, such as a preferredmaterial that is prominently featured in the designer user interface ora disfavored color and/or pattern that is disallowed by the designeruser interface. The designer user interface may be associated with a2-dimensional and/or 3-dimensional design and/or development tool.

At 204, a visualization tool may be influenced by the 2-dimensionaland/or 3-dimensional design and/or development tool. The visualizationtool may be influenced by the consumer data. Colors and/or designs thatare options and/or appear in visualizations created by the visualizationtool may be influenced by the consumer data.

At 206, interactive consumer experiences may be presented to consumersvia e-commerce websites. The interactive consumer experiences presentedto consumers may by influenced by the visualization tool. Theinteractive consumer experiences presented to consumers may byinfluenced by the 2-dimensional and/or 3-dimensional design and/ordevelopment tool. The interactive consumer experiences presented toconsumers may be influenced by the consumer data. Colors and/or designsthat are options and/or appear in the interactive consumer experiencesmay be influenced by the consumer data. Feedback from the interactiveconsumer experiences may be new consumer data at 200.

At 208, drop-on-demand (e.g., digital) and/or traditional manufacturingmay be influenced by the visualization tool. The digital and/ortraditional manufacturing may by influenced by the 2-dimensional and/or3-dimensional design and/or development tool. The digital and/ortraditional manufacturing may be influenced by the consumer data. Colorsand/or designs that are options and/or appear in the digital and/ortraditional manufacturing may be influenced by the consumer data.Feedback from the digital and/or traditional manufacturing may be newconsumer data at 200.

At 210, high speed manufacturing may be influenced by the visualizationtool. The high speed manufacturing may by influenced by the2-dimensional and/or 3-dimensional design and/or development tool. Thehigh speed manufacturing may be influenced by the consumer data. Colorsand/or designs that are options and/or appear in the high speedmanufacturing may be influenced by the consumer data. Feedback from thehigh speed manufacturing may be new consumer data at 200.

Designing and developing for fashion trends is currently fragmented andnot directly driven by consumer demand—designer. Creators will make bestestimate for what will be on trend and hope for the best. Thisconventional manner is not optimized. In accordance with the presentdisclosure, improved on-demand feedback loop may allow for thedata-driven forecasting of needed colorways and designs.

Referring to FIG. 3, a method for manufacturing an article isillustrated. The method may allow for customization. The method mayallow for dynamic pricing. The method may allow for dynamic lead timedetermination. The method may allow for dynamic delivery.

At step 310, consumer data comprising at least biometric informationassociated with one or more consumers may be received. One or morecomputing devices may receive consumer data comprising at leastbiometric information associated with one or more consumers. Theconsumer data may comprise consumer preference information.

At step 320, design inputs indicative of a design of an article may bereceived. One or more computing devices may receive design inputsindicative of a design of an article. The design of the article may bebased on the consumer data. The design inputs indicative of the designof the article may be consumer facing, such as design inputs for made tomeasure articles or personalized and/or custom articles. The designinputs indicative of the design of the article may be used for productdesign for mass produced articles. The design inputs indicative of thedesign of the article may comprise auto-pattern creation. The designinputs indicative of the design of the article may come directly from amanufacturer. The design inputs indicative of the design of the articlemay fit a design model. A “fit model” is a model utilized by a brand todesign a product line's sizing parameters, i.e. a standard collection ofdimensions scaled to each available size.

At step 330, output of interactive content may be caused to a userinterface associated with the one or more consumers. One or morecomputing devices may cause output of interactive content to a userinterface associated with the one or more consumers. The interactivecontent may comprise at least a representation of the design of thearticle.

At step 340, manufacturing data indicative of instructions associatedwith manufacture of the article may be outputted. One or more computingdevices may output manufacturing data indicative of instructionsassociated with manufacture of the article. The instructions may bebased on the design of the article. The outputting manufacturing datamay comprise outputting, to a digital print system, at least a portionof the manufacturing data. The manufacturing data may be provideddirectly to a manufacturer from a designer. The manufacturing data maybe provided directly to a manufacturer from a customer.

Coloration data indicative of a coloration feasibility may be received.One or more computing devices may receive coloration data indicative ofa coloration feasibility. The design of the article may be dependent onthe coloration data.

Sensors in clothing may detect when the clothing are being worn. Thesensors may communicate with applications executing on client devices.The applications may relay information from the sensors to a centralizedserver. The centralized server may comprise an application to determinetrend information, such as which colors, patterns, and/or fabrics arebeing worn most often. The centralized server may provide the determinedtrend information to a server associated with an e-commerce website or abrowser executing on a user device that is accessing the e-commercewebsite. The e-commerce website may make suggestions based on thedetermined trend information.

Referring to FIG. 4, a method for article development is illustrated. Atstep 410, consumer data comprising at least biometric informationassociated with one or more consumers may be received. One or morecomputing devices may receive consumer data comprising at leastbiometric information associated with one or more consumers. Theconsumer data may comprise consumer preference information.

At step 420, trend data indicative of a trend in one or more of articledesign or article coloration may be received. One or more computingdevices may receive trend data indicative of a trend in one or more ofarticle design or article coloration.

At step 430, output of one or more design options may be caused based onat least the consumer data and the trend data and via a user interface.One or more computing devices may cause output of one or more designoptions based on at least the consumer data and the trend data and via auser interface.

At step 440, design inputs indicative of a design of an article may bereceived. One or more computing devices may receive design inputsindicative of a design of an article.

In response to receiving the design inputs, types of fabrics may bepresented to a designer. The designer may select one or more of thetypes of fabrics presented. In response to the selected one or moretypes of fabrics, an integrated technology package may be created forthe designer. The integrated technology package may fit the designinputs and the selected one or more types of fabrics. An engineeringbill of material may be generated for the integrated technology package.The engineering bill of material may be generated on-demand.

Sensors in clothing may detect when the clothing are being worn. Thesensors may communicate with applications executing on client devices.The applications may relay information from the sensors to a centralizedserver. The centralized server may comprise an application to determinetrend information, such as which colors, patterns, and/or fabrics arebeing worn most often. The centralized server may provide the determinedtrend information to a server associated with a remotely accessibledesigner tool or a browser executing on a user device that is accessingthe remotely accessible designer tool. The designer tool may makesuggestions based on the determined trend information. A user may createa design based on the suggestions. The user may create an order based onthe design. A bill of material may automatically be generated based onthe order.

As an example, a method for article management may comprises receivingconsumer data comprising at least biometric information associated withone or more consumers. The consumer data may further comprises consumerpreference information. The method may comprise receiving design inputsindicative of a design of an article. The design of the article may bebased on the consumer data and/or other inputs. The method may comprisecausing output of interactive content to a user interface associatedwith the one or more consumers. The interactive content may comprise atleast a representation of the design of the article. The method maycomprise outputting article data comprising at least manufacturing dataindicative of instructions associated with manufacture of the article.The outputting manufacturing data may comprises outputting, to adrop-on-demand system (e.g., digital print system), at least a portionof the manufacturing data. The instructions may be based on the designof the article. The article data may be configured to be received by oneor more computing devices associated with one or more manufacturingprocesses, wherein the one or more manufacturing processes are updatedbased on at least the article data. The method may comprise receivingcoloration data indicative of a coloration feasibility, wherein thedesign of the article is dependent on the coloration data. The methodmay comprise generating a tech pack based on the design of the articleand a fabric selection. The method may comprise outputting a bill ofmaterial based on the design of the fabric. Other steps may be used. Asa further example, one or more methods may comprise wherein the articledata comprises nesting information indicating a spatial placement of oneor more parts of the article, and wherein the nesting information is atleast partially derived from one or more of: characteristics of amaterial used to form at least a portion of the one or more parts of thearticle, one or more treatments applied to a material used to form atleast a portion of the one or more parts of the article, a desired webspeed, or an operation performed by a pick and place system configuredto move the one or more parts of the article once separated from thematerial. The nesting information may be at least partially derived froma feedback loop associated with operations of the pick and place system.Data may be collected from any number of systems, subsystems, or devicesand may be shared upstream and/or downstream to effect updates in one ormore processes.

Order Aggregation and Batch Processing

Conventional order processing for digital printers does not account forthe entire manufacturing process of single order execution systems. Thisis largely driven by a fragmentary value chain where each process takesinto account efficiencies for their respective processes, but not theoverall manufacturing process and its associated holistic cost. Thepresent disclosure provides dynamic nesting optimization. As an example,dynamic nesting optimization may comprise the individual consumerorder—specifically the theoretical minimum order quantity (MOQ) ofone—and batching order components to maximize production and deliveryspeed back to the end customer within business-directed productperformance, unit costs/margins, and sustainability parameters.

In single unit order execution, individual components can have vastlydifferent levels of ink applied. In the subsequent washing processes,components with high levels of ink can cross-contaminate adjacentcomponents with low levels of ink (e.g. a bright red component adjacentto a white component) leading to off quality. In accordance with thepresent disclosure, by analyzing the levels if ink required to printeach discrete component, a nested pattern can be created that startswith the lowest levels on ink and builds to the highest levels of ink.Therefore, dark and saturated components (e.g. a bright red component)will be adjacent to components that are also dark in color therebyhiding cross contamination from a dark color to a light color. When saidmaterial is running through the washing process, the lightest colorswill go first (when water on the washer is cleanest) and dark colors golast. This may allow for using less water and chemistry for lightercolors and make the overall process more efficient and sustainable.

Conventional production planning processes do not account for optimizingsmall batches (as small as a single unit) into large runs that takeadvantage of both digital manufacturing processes (e.g. digitalprinting) as well as conventional ‘continuous’ production processes(e.g. drying, washing). In accordance with the present disclosure, rulesmay be used that aggregate and organize small batches into largerbatches while taking into account different downstream routings wherebysmall batches can be aggregated for common processes, and then splitback into smaller batches for separate routings in a way that can beefficiently scheduled in production.

Nesting

Current process of creating RIP and print job files do not account forseparate throughput speeds for actual printing or downstream processing.The present disclosure may integrate considerations for printing,finishing, assembly and other manufacturing processes to batchthroughputs for greater efficiency and overall speed.

Nesting optimization in digital printing processes currently used are onthe order of 60%-70%, which is very poor (waste of 30%-40% of material)versus materials optimization for conventional apparel manufacturing onthe order of 80%-95% (waste of 5%-20% of material). Nesting optimizationneeds to improve in the digital printing space in order to make theprocess sustainable and feasible at commercial scale. The presentdisclosure may use an optimized nest of components that are produced ondemand to approach the efficiency of conventional manufacturing on theorder of 80%-95% materials utilization.

FIG. 5 shows an example set of articles of clothing illustratingnesting. A first article of clothing 500 may comprise two sets ofcolors. A first color may comprise a top half of the first article ofclothing 500. A second color may comprise a bottom half of the firstarticle of clothing 500. A second article of clothing 502 may comprisetwo sets of colors. A top-right half of the second article of clothing502 may comprise the second color. A bottom-left half of the secondarticle of clothing 502 may comprise a third color. A third article ofclothing 504 may comprise one color—the second color. A fourth articleof clothing 506 may comprise one color—the first color. A fifth articleof clothing 308 may comprise one color—the third color.

Nesting may comprise arranging the articles of clothing 500, 502, 504,506, 508 such that colors of adjacent borders of the articles ofclothing 500, 502, 504, 506, 508 may be similar. The third article ofclothing 506 may be arranged to be adjacent to the top half of the firstarticle of clothing 500. The bottom-left half of the second article ofclothing 502 may be arranged to be adjacent to the fifth article ofclothing. Two or more of the bottom half of the first article ofclothing 500, the top-right half of the second article of clothing 502,and the third article of clothing 504 may be arranged to be adjacent.

In an illustrative example, a garment part may be transferred and/orstacked (aggregated) using a mechanical arm (or robot). A plurality ofsuch mechanical arms with the corresponding end-effectors may comprise apick-and-place production line. The pick-and-place process (involvingtransferring and stacking) is typically much slower than other processesin the envisioned system and can therefore be considered a “bottleneck”.However, the process may be improved with a nesting protocol thatconsiders the specific arrangement and transfer characteristics of themechanical arms so as to maximize the throughput. A nesting arrangementmay change depending on, for example: the fabric characteristics (suchas porosity, stiffness, etc.); the type of treatments applied to thefabric; the desired web speed; additional operations performed by themechanical arms; etc. The starting nesting arrangement may be performedby a human or a nesting software. As the pick-and-place process occurs,the mechanical arms may send a feedback to a computer that may result inan altered nesting arrangement maximizing the overall throughput and/orpick-and-place speed.

As an example, parts of a garment may be grouped into sizes, forexample: small and large. Thresholds for grouping and the number ofgroups may be determined for a particular operation or desired output.As far as textile materials are concerned, a different pick-and-placeapproach may be used for small parts compared to the approach used forlarge parts. A special nesting may be created that considers varioustime delays associated with any particular mechanical arm (such as forexample, adjusting of the size and arrangement of grippers so that largeparts can be picked up immediately after the small parts, etc.). Thepick-and-place system mentioned above may be configured with nestingoptimization to allow the system to handle multiple smaller parts atonce or as a series in time. The pick-and-place system may be configuredwith nesting optimization to handle individual large parts or a mixtureof small and large parts. The pick-and-place system may be configuredwith nesting optimization to maximize the throughput speed and fabricutilization. Other optimizations may be used.

Component Manufacture

FIG. 6 shows an example diagram of a component manufacturing process. At600, printed fabric may be received. The fabric may have been printed atthe printing step at 158 in FIG. 1. The fabric may have been dried atthe post-print drying step at 166 in FIG. 1. The fabric may comprise acustom upper portion of a shoe. The fabric may comprise rows and/orcolumns, wherein each row and column combination may comprise identicalprinting. The fabric may comprise a cotton canvas.

At 602, the fabric may be finished. Finishing the fabric may comprisesteaming the fabric. The fabric may be steamed at the fixation/steamingstep at 170 in FIG. 1. Finishing the fabric may comprise washing thefabric. The fabric may be washed at the post-print washing step at 174in FIG. 1. Finishing the fabric may comprise drying the fabric. Thefabric may be dried at the post-print drying step at 178 in FIG. 1.

At 604, a liner may be applied to the fabric. The liner may be glued tothe back of the fabric. The fabric may be printed and finished cottoncanvas. Alternative or additional methods may be used.

At 606, components may be cut in the fabric. A laser, router, or knifemay be used to cut the components in the fabric. Partial chads may beleft in the cut components. Each row and column combination may becompletely cut.

At 608, the completely cut fabric (row and column) combinations may bestacked. The completely cut fabric may be stacked such that thepartially cut components of one fabric layer line up with correspondingpartially cut components of a fabric layer stacked above and/or below.The stacked fabric may be sent to an assembler for assembly.

Color Control

Conventional coloration methods are largely dependent on manualprocesses with multiple, time-consuming iterations through a extendedperiod of trial and error. The present disclosure may combine precisionsubstrate characterization data, chemical profiles from inks by color,and precision wet finishing data to preempt the extended trial and errorprocess.

Conventional design tools are fragmented, incompatible, and in many wayscompletely ring-fenced from the manufacturing process, necessitating anextended trial and error process to produce as designed, creating theneed to create changes to original design to manufacture. The presentdisclosure comprises an integrated manufacturing job file creationfunction that presents customers, designers and other end users onlyachievable design and material attributes, excludes colors andcharacteristics that are untenable within allowed performance attributesand standards, thereby seamlessly creating a manufacturing job filedirectly from the inputted design.

Typically conducted as a separate ad hoc process as a post processingQA/QC function—it takes too long and happens too far from the colorationprocess. Other manufacturers fail to integrate data up and downstream inthe value chain, i.e. desired end-color, substrate construction andfollow-on wet processing and lamination processes. The presentdisclosure may integrate this into our inline coloration and fixationprocesses to more proactively inform color-matching and repeatability.

PRETREATMENT Foam Application

In the direct-to-fabric digital printing of the textiles Industry,pre-treatment chemistries are applied to textiles in open width formthrough a process referred to as padding whereby the entire textile isdipped in chemistry and the excess is squeezed out prior todrying/fixing of the chemistry. Under the conventional process, theamount of moisture absorbed by the textile can range from 70% to >100%of the weight of the textile (referred to in the industry as “Wet PickUp”), and all of this moisture must be evaporated in an energy intensiveprocess when drying the textile prior to subsequent processing. Theother problem with conventional padding of pre-treatment chemistry isthat chemistry is applied on and through the entire textile when in mostcases the chemistry is only needed on the surface that will be printed.Therefore, the conventional process requires the use of more energy,water, and chemistry than is needed to add value in subsequentprocesses. The textile industry is the second largest consumer of freshwater in the world, and one of the largest polluters of surface waterafter the agricultural industry. The industry is seeking novel ways toreduce water, energy and chemical consumption.

Foam application of chemistry has been in commercial use for severaldecades. In the nascent industry of direct to fabric digital printing,production speeds are increasing to a level where the industry isgrowing at a fast rate and gaining attention from the investmentcommunity. In the present disclosure, a process comprises pre-treatmentchemistry applied via foam applicator which has several benefits ofimportance to the textile industry, for example: reduced energyConsumption, reduced water consumption, reduced chemical consumption,more accurate application of chemistry where it is needed, reducedchemical load on wastewater treatment systems. It has also beendemonstrated that deeper, richer colors can be achieved through the foamapplication process versus conventional process.

FIG. 7A shows positive results from foam-applied pretreatment acrossfour preliminary chemical formulations. These preliminary formulationsdemonstrate higher average results in a number of categories compared tothe average results in control cases or conventional processes. R is thereflectance at the wavelength of maximum absorption in a decimal way(20% R=0.20 R)

All 4 samples (3C, 5B, 2B & 2D) are foam applied and the resultscompared with the corresponding conventional padded or pad-appliedsample. For example samples 3C and 5B were compared with padded sample(Pad 1 below) and 2B and 2D were compared with another padded sample(pad 2 below)

SWL value>100% associated with the foamed samples means that highercolor yield was achieved by that foam formulation and conditions,compared at least to the conventional samples.

FIG. 7B illustrates positive results with four different chemicalformulations, where 2B and 2D demonstrate a performance outcome similarto baseline control case, 3C and 5B demonstrate improved performanceover the results of the control and other formulation variables.

The present disclosure comprises formulations for foam treatment, suchas the following (although other chemistries may be used):

Pad 1 Urea 10%  100 g/kg  Alkali (Sodium carbonate) 1% 10 g/kg Migrationinhibitor (Thermacol MP) 10%  100 g/kg  Pad 2 Urea 10%  100 g/kg  Alkali(Sodium Carbonate) 2% 20 g/kg Migration inhibitor (Prepajet Uni) 8% 80g/kg Reduction inhibitor (Lyoprint RG) 2% 20 g/kg

Foam treatment for Durable Water Repellant (DWR) may be used. As anexample, FIG. 7C shows a DWR formulation with specific parameters forfoam application onto a polyester substrate whereby performanceimprovements are demonstrated to produce a 50% reduction in chemistrysavings combined with a possible 80-85% reduction in chemistryconsumption.

Referring to FIG. 7D, a method for pretreating textile is illustrated.At step 710, a textile may be received. A materials manufacturer mayreceive a textile. The step 138 in FIG. 1 may comprise the step 710.

At step 720, a select area of the textile that is to be printed may bedetermined. A materials manufacturer may determine a select area of thetextile that is to be printed. The step 140 in FIG. 1 may comprise thestep 720.

At step 730, an applicator may be caused to apply a foam chemistry tothe select area of the textile. A materials manufacturer may cause anapplicator to apply a foam chemistry to the select area of the textile.Application of the foam chemistry to areas of the textile outside theselect area may be minimized. The step 140 in FIG. 1 may comprise thestep 730.

At step 740, the select area of the textile may be dried such that asurface of the select area is capable of being printed. A materialsmanufacturer may dry the select area of the textile such that a surfaceof the select area is capable of being printed. The step 140 in FIG. 1may comprise the step 740.

A materials manufacturer may receive a textile and a corresponding jobfile. The job file may indicate that a particular area of the textileshould get printed. The materials manufacturer may cause the particulararea of the textile to receive a foam pretreatment. The materialsmanufacturer may dry the particular area of the textile. The materialsmanufacturer may cause the particular area of the textile to be printedas dictated by the job file.

Plasma Pre-Cleaning/Activation

Textile materials must be thoroughly cleaned in order to optimize thewettability and adhesion of chemistry (e.g. Durable Water Repellantfinishes, colorants, polymer coating, lamination etc.). As environmentalrestrictions have intensified over the use of solvents and surfactants,it is increasingly difficult to achieve the same level of cleanlinessobtained with the aggressive chemicals of the past (e.g. solvents). Mostcleaning of textiles today is water based using a great deal of heatenergy and the most benign detergent chemicals possible. Unfortunately,the modern cleaning systems, while environmentally friendly do not leavethe textiles free of contaminants that can interfere with the colorationand finishing of textiles. Applying chemicals to contaminated fabricsoften leads to poor performance, poor durability of functional finishes,or the need to use more chemistry to achieve a passing rating than wouldbe needed if the fabric were completely clean. Atmospheric plasmatreatment can change the surface chemistry and topography of fabricand/or materials to improve adhesion properties to different materials.Each plasma carrier gas can provide different surface chemistry andsurface topography.

In accordance with the present disclosure, a corona plasma process mayuse ionized gases to vaporize remove (decompose) contaminants (oils,waxes, etc.) on the surface of a textile. It is a waterless process andresults in a cleaner surface that is easier to ‘wet out’ by changingsurface chemistry and surface energy of the fabric and or material withwater based chemistries. A corona plasma unit can be placed prior to thechemical application step to aid in chemical penetration (wettability)as well as activation of the textile surface. Plasma can be used toincrease the performance of some chemical applications (e.g. DWR) aswell as achieve deeper, more saturated colors, using less dyes andchemicals.

Referring to FIG. 8, a method for pretreating textile is illustrated. Atstep 810, a textile may be received. A materials manufacturer mayreceive a textile. The step 138 in FIG. 1 may comprise the step 810.

At step 820, at least a portion of the textile may be conditioned toremove one or more contaminants from at least the portion of the textileusing a plasma. A materials manufacturer may condition at least aportion of the textile to remove one or more contaminants from at leastthe portion of the textile using a plasma. The conditioning at least theportion of the textile may activate a surface of the textile. The plasmamay comprise a corona plasma. The step 140 in FIG. 1 may comprise thestep 820. The step 146 in FIG. 1 may comprise the step 820.

At step 830, one or more chemistries may be applied to at least theportion of the textile. A materials manufacturer may apply one or morechemistries to at least the portion of the textile. The step 140 in FIG.1 may comprise the step 830. The step 146 in FIG. 1 may comprise thestep 830. Activation of the surface of the textile may improve aperformance of the one or more chemistries as compared to an unactivatedsurface of the textile with the same one or more chemistries appliedthereto.

A materials manufacturer may receive a textile with contaminants on aparticular area of the textile. The materials manufacturer may removethe contaminants by using plasma on the particular area. The materialsmanufacturer may apply one or more chemistries to the particular area ofthe textile to activate the particular area of the textile.

Digital Coloration Drop-on-Demand (e.g., Digital Printing)

In the apparel industry, conventional products are currentlymanufactured under a forecasted model where wholesalers and retailersplace orders for apparel & footwear against a forecast prior to aconsumer actually purchasing the end product. Under this scenario,products are manufactured using large batches of inputs (e.g. materialssuch as textiles) that are successively broken down into smaller andsmaller batches until the final process whereby the end product iscomplete as a ‘batch of 1’ unit, an illustrative example shown in FIG.9B. Under this system, the unique identifier for the final product isnot assigned until the very last step in the process. In one scenariowhere products will be manufactured under a ‘mass customization’ model -the consumer may purchase the end product prior to its manufacture.Under this model, it is critical that each component of the end productbe identified throughout the manufacturing process in order to keeptrack of the order from inception to delivery. Identifying eachcomponent can be generated at the digital printing step using uniqueidentifiers such as barcodes or QR codes. However, most consumers do notwant to see the unique identifiers on the final product, an illustrativeexample show in FIG. 9B. In addition, material utilization is a keydriver of the efficient and sustainable use of raw materials. In orderto solve the identification problem while not using excessive material,the unique identifiers need to be placed on each component in a legibleway, but without being visible to the consumer.

In the present disclosure, quality control may be implemented viainvisible registration points, such as using an invisible ink that maybe viewed via computer vision or some other process. The present systemsand methods may embed data through unique dithering pattern. Forexample, the present disclosure comprises the application of uniqueidentifiers (e.g. Bar Codes, QR Codes, illustrative example shown inFIG. 9B) using methods that are ‘invisible’ to the consumer, while beingreadable for the manufacturing process from digital printing throughpoint of sale. The present disclosure comprises the use of uniqueidentifiers that are applied to each component using invisible ink thatis readable by machine vision, but using ink that is outside of thevisible spectrum of human perception (e.g. Ultraviolet, infrared, etc.).

Attribution and Traceability

In the apparel industry, ‘at scale’ industrial production does notsupport digital bespoke manufacturing driven by consumer generated oraggregated content. In order to manage unique, single unit workflow thatcan be used for making claims and proving provenance of product andcomponents, requires a digitally generated marking system for everytextile component that can be tracked from the point of generationthrough to point of sale, whereby the marking can be linked to theentire value chain through a manufacturing integration and intelligencesystem. In the present disclosure, the systems and methods may embedcustomer order data with visible and non-visible attribution via digitalprinting using visible and/or non-visible codes.

A method for attribution and/or traceability may comprise receivingorder data associated with one or more first consumer orders. One ormore unique identifiers (UIDs) may be disposed on at least a portion ofa material. The one or more unique identifiers may be invisible to ahuman eye and visible with the aid of a predetermined vision method. Theone or more unique identifiers may represent article data comprising atleast a portion of the order data. One or more methods may compriseprocessing, via one or more manufacturing processes, the material toform at least a portion of an article. The article data represented bythe one or more unique identifiers may be updated based on eachmanufacturing process to comprise information associated with therespective manufacturing process. Each of (or one or more of) themanufacturing processes may comprise reading the article data andadjusting one or more actions associated with the respectivemanufacturing process based on the article data. The article data mayindicate a provenance of the article. As used herein, article data maybe or comprise other data such as nesting data, order data, color data,etc.

A method for attribution and/or traceability may comprise receivingorder data associated with one or more first consumer orders. One ormore unique identifiers may be disposed on at least a portion of amaterial. The one or more unique identifiers may be visible to the humaneye and configured to be concealed via one or more manufacturingprocesses. The one or more unique identifiers may represent article datacomprising at least a portion of the order data. Other data may berepresented. One or more methods may comprise processing, via the one ormore manufacturing processes, the material to form at least a portion ofan article and to conceal at least a portion of the one or more uniqueidentifier. The article data represented by the one or more uniqueidentifiers may be updated based on each manufacturing process tocomprise information associated with the respective manufacturingprocess. Each of (or one or more of) the manufacturing processes maycomprise reading the article data and adjusting one or more actionsassociated with the respective manufacturing process based on thearticle data. The article data may indicate a provenance of the article.

Digital marking of apparel/footwear components requires unique markersand/or serialized unique markers (e.g., FIG. 9B) that are sufficientlylarge enough to be legible via machine vision in order to enableautomated manufacturing (reliable legibility). The size of digitalmarkers vary by substrate—e.g., very flat, even substrates can be markedwith smaller markers due to the physics of the flatter surface, whereassubstrates with a larger degree of Z direction texture (e.g. seersuckerweave, waffle knit) require relatively larger markers due to the physicsof the light reflecting off the substrate surface. In the presentdisclosure, an MII (Manufacturing Integration & Intelligence System) maygenerate unique digital identifiers (e.g. QR Code, Bar-Code) that aregenerated to be reliably legible depending on the data collected on thegiven substrate. The present disclosure may automatically select theappropriate sized marker based on the substrate data and the size of theprinted component.

Current product storytelling requires months or years of planning withthe upstream supply chain whereby proving the provenance of inputs (forexample, organic content, recycled content) is managed throughunderwriting or legal documentation to manage risk as opposed to adefined, digital chain of custody. As the market moves towards smallerbatch sizes, and higher degrees of customization, it becomesincreasingly difficult to trace inputs and processes to make marketingclaims. The systems and methods of the present disclosure may generateunique identifiers that can be accessed digitally (e.g. QR Codes, BarCodes) to connect the consumer with the history and provenance of theend product whereby the inputs and ‘ingredients’ are compiled as theproduct moves through the supply chain and will be accessible to the endconsumer through interacting with the unique identifiers (e.g. throughmobile devices, scanners, digital cameras, etc.).

In the apparel and footwear industries, textile substrates, while being‘engineered’ materials, can have a large degree of variation indimensional change of the substrate through steps in the manufacturingprocess, particularly in the finishing stage of processing. Thedimensional change can be the result of various factors: changes inmoisture/wetting of material; mechanical forces in wet and dryprocessing such as stretching or compacting of material;permanent/semi-permanent changes in thermoplastic substrates as theresult of thermal fixation (heat setting), application of chemistries(e.g. coatings, etc.). Dimensional changes manifest not only at themacro level (batch to batch) but also at the micro level within a singleyard or meter of a substrate making localized predictions of dimensionalchange unpredictable. Because of aforementioned dimensional changes, therelationship of registration marks for the cutting process of individualcomponents (applied in previous processes, for example on a digitalprinter) can shift significantly through processing such that cuttingbased off of the initial design dimensions will result in componentsthat out of specification. To solve this problem, a much more robustprocess for identifying dimensional change prior to cutting is necessaryin order to provide accurate cutting and also provide a data feedbackloop to improve prediction of dimensional change and/or identify qualityissues using machine vision. Adding dimensional reference points acrossthe width and length of a textile web is critical. Placing high densityreference marks on a an end product is not commercially acceptable tothe consumer, so marks that are invisible to consumers (outside of thevisual spectrum) yet visible to machine vision is critical for highspeed single ply cutting of textiles. Markers that are large enough tobe reliably detected would likely be objectionable to the averageconsumer if said markings were visible on every component of anapparel/footwear product. In the present disclosure, invisible ink maybe used to create registration points that track changes to the originalpattern as a quality control measure. This can be corrected withincutting process or the order will be referred back to the queue in apreformatted job file to reproduce.

Conventional components are generally cut from monolithic prints - thiscreates wasted ink, finishes, and adds difficulty to recycling unusedmaterial (e.g., fabric or other components). In the present disclosure,precision application of finishing to allow for the recycling of unusedfabric

Adhesives are traditionally applied monolithically and in an analogmanner. This creates a high level of waste in both expenditure of excesschemistry and prohibits the ability to recycle unutilized material(e.g., fabric or other components). The present disclosure may utilizedigital printing/extrusion of adhesives, using proprietary formulation,to apply chemistry only where needed. The platform may identify visualregistration points, reference to layers in a digital tech packdatabase, and use precision application of chemistry only to thenecessary areas of the respective component-level (or engineered) print.

FIG. 9A presents an illustrative example of component level printing,combined with precision laser cutting, to facilitate ease of assemblyand automation. The demonstration of component level engineeredprinting, nested as pairs and batched according to downstreamprocessing, enables flexibility in downstream sewing and assembly—wherebenefits may be realized in the printing of the order, whereas thedownstream sewing and assembly is largely commoditized in practice. FIG.9B demonstrates an example, vis a vis QR code, for up and downstreamtraceability. QR code is representative of dithering patterns and‘invisible’ in formulations to contain underlying data for the purposesof supply chain sustainability and CSR, to facilitate manufacturingthrough shipping process, as well as creating an opportunity formarketing and attribution. FIG. 9C shows incremental improvementsdemonstrated in diagram, e.g. precision cuts to leave remaining uncutchads, translates to material savings in direct labor costs, whileincreasing underlying product quality and consistency compared toexisting manufacturing methods. FIG. 9D-E demonstrate an example oftraditional manufacturing processes eliminated by the operationalizationof engineered printing and digital manufacturing writ large. In thiscase, the simple replacement of tag (with applicable care and sizinginformation and requisite country of origin data) with digitally printedinformation eliminates direct labor costs attributed to the item'smanufacture, while also presenting an opportunity to prevent acountermeasure against fraud and circulation of counterfeit goods.

Alternatively or in addition to being used as carriers of information,UIDs can also be used to “grade” or evaluate a process step orprocesses. For example, if a particular UID has a reflective propertythen measuring the reflectivity prior to the PU coating application(“the process”) and again after the completion of the process wouldyield “local” or garment-part-specific information about the thicknessand/or quality of the applied PU coating. In other instances, the UIDswould themselves undergo change (for example, a color change orvisibility change corresponding to a maximum and/or minimum temperatureused in the process or indicating a particular temperature range).Evaluation of other production process properties could be envisioned.These UIDs can be applied within the seam allowance of each garment partand/or within a gutter region of a fabric roll.

Wet Finishing

Conventional finishing processes are generally fragmented processes,physically and digitally separate from the printing process. The currentstate presents an extremely long feedback loop. Currently manuallyexecuted with high degree of variance (especially from one site toanother). In the present disclosure, in-line spectrophotometer may beimplemented to measure variance and create algorithm to optimizesettings for on-premises and networked manufacturing. For example, thesystems and methods may use aggregated data to create baseline recipethat can be adjusted for other manufacturing sites and their respectiveconditions, i.e. water quality, chemistry, ambient conditions, etc. readdata from previous process and setting, then write conditions from thispart of the process

FIG. 10 shows an example diagram of a wet finishing process. The wetfinishing process may comprise a process data collection 1000 and aproduct data collection 1050. The process data collection 1000 mayreceive and/or extract data from a digital input and/or order 1040. Theprocess data collection 1000 may be in communication with the productdata collection 1050.

The process data collection 1000 may comprise a process recipe database1010. The process recipe database 1010 may comprise substrate data 1012,coloration data 1014, hand feel data 1016, finishing data 1018, etc. Theprocess data collection 1000 may comprise data regarding various processsteps, such as a substrate pre-treatment step 1020, a substratecoloration step 1022, a substrate steaming step 1024, a substratewashing step 1026, a substrate curing step 1028, a substrate drying step1030, a substrate functional finishing step 1032, a substrate tumblingstep 1034, etc. Data regarding the various process steps may be obtainedfrom a sensor. Data regarding the various process steps may be obtainedfrom a spectrometer. Data regarding the various process steps may beobtained from an inline spectrophotometer. The inline spectrophotometermay measure variance in the data regarding the various process steps.Measured variances in the data regarding the various process steps maybe used to create an algorithm to obtain more optimal settings. Theprocess data collection 1000 may comprise settings and/or conditions.The settings and/or conditions may be attributed to direct performanceoutputs. The settings and/or conditions may be associated with anin-line dryer, a steamer, a washer, a Stenter Frame, etc.

The product data collection 1050 may comprise a product feedbackcollection 1060. The product feedback collection 1060 may comprise dataregarding various aspects of a product, such as non-metameric colormatching feedback 1070, substrate hand feedback 1072, substrate airpermeability feedback 1074, substrate water permeability feedback 1076,substrate light reflectance feedback 1078, substrate heat absorbancefeedback 1080, substrate heat retention feedback 1082, etc. Dataregarding the various aspects of the product may be obtained from asensor. Data regarding the various aspects of the product may beobtained from a spectrometer. Data regarding the various aspects of theproduct may be obtained from an inline spectrophotometer. The inlinespectrophotometer may measure variance in the data regarding the variousaspects of the product. Measured variances in the data regarding thevarious aspects of the product may be used to create an algorithm toobtain more optimal settings. The product data collection 1050 maycomprise settings and/or conditions. The settings and/or conditions maybe attributed to direct performance outputs. The settings and/orconditions may be associated with an in-line dryer, a steamer, a washer,a Stenter Frame, etc.

Digital Finishing Component-Level Application of DWR (e.g., Fluoro Freeor Conventional Fluoro)

Conventional DWR processes are conducted in batches with monolithicapplication of chemistry whereby the chemistry is applied across theentire web of textile material with the same level of application acrossthe entire surface. The problem with this approach is that chemistry isused across material that ends up as waste, and there is no way tocontrol the level of repellency in an ‘engineered’ approach to generatenew performance applications. In the present disclosure, digitalapplication of DWR at component level and in roll-to-roll process willallow for engineered patterns of moisture management that can bedigitally enabled. This will lead to a more sustainable process wherebyless chemistry is used to create performance. Also, fabric that ends upas waste after the cutting process can more easily be recycled due tothat waste being free of chemistry contaminants. Also, with the digitalapplication of the chemistry, new performance functions can be enabledby the engineered placement of chemistry that can be scaled acrossdifferent sized components to enable single unit customization.

Engineered Application of Chemistry

Convention application of chemistry covers all of fabric, creatingsignificant waste in chemicals, and prevents recycling of un-utilizedfabric. In the present disclosure, precision digital application ofchemistry such as adhesives may reduce chemical use, save costs, andallow for recycling of unused fabric.

Cutting

Typical conventional methods cut pre-programmed patterns from amonolithic print, necessitating repeats of set component patterns andleads to wasted ink and impedes scaled customization. In the presentdisclosure, dynamic recognition of cut patterns allows for increasedoverall throughput, reduced waste, and mass customization.

Current automated single ply cutting in the textile material industrydoes not have sufficient throughput to scale in the apparel/footwearindustry. Conventional technologies utilize gantry driven X/Y axisequipment, generally with a mechanical knife and on occasion with laserenergy. The systems and methods of the present disclosure may utilizehigh speed galvanometer driven lasers which have a throughput that is 2orders of magnitude higher than gantry driven systems. FIG. 11illustrates a design for an early high-speed single-ply galvanometerdriven laser cutting prototype to far surpass existing conventionaltextile cutting methods.

Traditional wet finishing processes such as post-print steaming,washing, and stenting create distortion in fabrics that are non-linearand difficult to predict consistently (particularly on knits). Thesedistortions prevent the ability to consistently print both patterns andcomponents with precision. The systems and methods of the presentdisclosure may match a database of pre-graded component shapes andpatterns and make cut adjustments to correct for observed shapedistortions while tuning recipe changes for the next print iteration.FIG. 12 an infeed portion of the FIG. 11, where vision recognition andreal-time job file correction data is collected and transmitted.

Printing at the component level creates inefficiencies in down processsorting and handling where engineered print components areindecipherable from waste. The systems and methods of the presentdisclosure may nest prints and cut components such that unused substrateremains attached as a web. This web of unused substrate is wound up fromthe belt, leaving the relevant cut components to be sorted kitted andassembled, with the waste effectively batched and recycled. FIG. 13illustrates a nip roller for waste removal and subsequent downcycling orrecycling.

Conventional cutting methods include cutting components by hand withmechanical tools in analog function or automatic cutting of materialusing a gantry driven knife, router, or laser. This labor intensiveprocess creates outsized inefficiencies for customization in particular.The systems and methods of the present disclosure provide nesting byunique customized order, batching, and precision laser cutting to leaveconnecting chads to keep together enabling hand separation from block.

A method for cutting registration may comprise analyzing, using computervision, a first pattern configuration disposed on printed material. Oneor more methods may comprise implementing a finishing process on theprinted material, resulting in a second pattern configuration differentfrom the first pattern configuration. One or more methods may compriseanalyzing, using computer vision, the second pattern configurationdisposed on the printed material. One or more methods may comprisedetermining, based on the first pattern configuration and the secondpattern configuration, cutting control information. One or more methodsmay comprise sending the cutting control information to a cutting systemto facilitate cutting of the printed material. The cutting system maycomprise a high-speed single-ply galvanometer driven laser cuttingsystem. The finishing process comprises steaming the material, washingthe material, and/or drying the material. The finishing process maycomprise a digital finishing processes. The cutting control informationmay be dependent upon a type of the material. One or more methods maycomprise batching one or more customer orders into a batch and nesting aplurality of article components on the printed material based on thebatch.

A method of cutting may comprise batching one or more customer ordersinto a batch, nesting a plurality of article components based on thebatch, and cutting the nested components from a substrate such that oneor more tabs connect the cut components to a portion of the substrate.Prior to the cutting step, a method may comprise analyzing, usingcomputer vision, a first pattern configuration disposed on thesubstrate, implementing a finishing process on the substrate, resultingin a second pattern configuration different from the first patternconfiguration, analyzing, using computer vision, the second patternconfiguration disposed on the substrate, and determining, based on thefirst pattern configuration and the second pattern configuration,cutting control information. The cutting step may be implemented basedat least on the cutting control information. The cutting step mayimplemented using a high-speed galvanometer driven laser cutting systemto cut a single ply. The finishing process may comprise one or more ofsteaming the material, washing the material, or drying the material. Thefinishing process may comprise one or more of belt compacting,mechanical compacting, decatising, spongeing, sanforizing, relaxeddrying, continuous tumbling, or batch tumbling. The finishing processmay comprise one or more sueding, shearing, raising, open widthcompacting, tubular compacting, calendaring, vaporizing, spongeing,atmospheric plasma finishing, continuous cecatising, semi-continuousdecatising, crabbing, coating, laminating, embossing, tensionlessdrying, relaxed drying, tentering, stentering, napping, brushing,singeing, beetling, heatsetting, thermofixing, fulling, digitalprinting, roller printing, scutching, sputtering, or Schreinering. Thecutting control information may be dependent upon a materialcharacteristic comprising one or more of a type of material, a thicknessof material, a mass per unit area of material, a porosity of material,or a yarn characteristic.

FIG. 14 shows an example diagram of a laser cutting process. At 1400,fabric may be observed. The fabric may be digitally printed fabric. Thedigitally printed fabric may comprise patterns. Robot vision may be usedto capture pattern dimensions. The captured pattern dimensions maycomprise original pattern dimensions. The captured pattern dimensionsmay comprise pre-finishing pattern dimensions.

At 1402, fabric finishing processes may be performed on the fabric.Fabric finishing processes may comprise steaming the fabric. The fabricmay be steamed at the fixation/steaming step at 170 in FIG. 1. Fabricfinishing processes may comprise washing the fabric. The fabric may bewashed at the post-print washing step at 174 in FIG. 1. Fabric finishingprocesses may comprise drying the fabric. The fabric may be dried at thepost-print drying step at 178 in FIG. 1. The fabric may undergotraditional and/or digital finishing processes. The fabric finishingprocesses may result the fabric comprising altered pattern dimensions.

At 1404, fabric changes may be observed. Robot vision may be used tocapture pattern dimensions of the fabric after the fabric finishingprocesses. The captured pattern dimensions of the fabric after thefabric finishing processes may comprise the altered pattern dimensions.The captured pattern dimensions of the fabric after the fabric finishingprocesses may comprise post-finishing pattern dimensions. The alteredpattern dimensions may be compared with the original pattern dimensionsto obtain a delta (e.g., change, alteration, etc.). The delta may beprovided to a laser control system. The laser control system may use thedelta to more accurately and more precisely cut the patterns from thefabric.

Referring to FIG. 15, a method for cutting registration is illustrated.At step 1510, a first pattern configuration disposed on printed fabricmay be analyzed using computer vision. A materials manufacturer mayanalyze a first pattern configuration disposed on printed fabric usingcomputer vision.

At step 1520, a finishing process may be implemented on the printedfabric, resulting in a second pattern configuration different from thefirst pattern configuration. A materials manufacturer may implement afinishing process on the printed fabric, resulting in a second patternconfiguration different from the first pattern configuration.

At step 1530, the second pattern configuration disposed on the printedfabric may be analyzed using computer vision. A materials manufacturermay analyze the second pattern configuration disposed on the printedfabric using computer vision.

At step 1540, cutting control information may be determined based on thefirst pattern configuration and the second pattern configuration. Amaterials manufacturer may determine cutting control information basedon the first pattern configuration and the second pattern configuration.

At step 1550, the cutting control information may be sent to a cuttingsystem to facilitate cutting of the printed fabric. A materialsmanufacturer may send the cutting control information to a cuttingsystem to facilitate cutting of the printed fabric.

A materials manufacturer may receive fabric with a printed design. Thematerials manufacturer may use computer vision to capture an originalprinted design on the fabric. The fabric may undergo a finishingprocess. The materials manufacturer may use computer vision to capturean altered printed design on the fabric. The materials manufacturer mayuse the original printed design and the altered printed design todetermine a delta. The materials manufacturer may provide the delta to acutting system. The cutting system may use the delta to cut the fabricwith the altered printed design.

Material Handling

Traditional automation methods for handling and kitting functions withinthe textile and apparel industry are highly specialized to specificproduct applications. As traditional apparel and footwear assembly linesare capital intensive and product specific, high volume and throughput,with low labor costs, are needed to justify investments. This investmentthreshold is prohibitively steep for most businesses, generallypreventing manufacturing operations within high-cost developed markets.

In the present disclosure, as illustrated in FIGS. 16-17, a design forautomated kitting and assembly creates a flexible platform that can bereconfigured for different product types and categories as well as anability to adjust to variances in respective throughput speeds andvolume. For example, the pick & place robot types (e.g. SCARA vs delta)in variable concentrations and overlap radiuses through the use of anoverhead rail system. Throughput speed/volume can be moderated throughthe use of a tray and sorting system that allows for pieces to be sorteddynamically, into either a stack of like components or as a batchedorder, in a way that simplifies the problem set to be navigated viavision recognition and minimized mechanical distance travelled. Otherexamples of platform agility can be found with interchangeable endeffectors (electrostatic, water, vacuum, etc.) to account for substratetype, the conveyor expanded to manage larger cut components, and traysswapped for envelopes for the off-site assembly of custom orderselsewhere.

Conventional article management processes remain siloed and ring-fencedfrom printing, nesting, and batching considerations. This createsprohibitive cost inefficiencies when printing at component level inparticular. The present disclosure may treat the end-to-endmanufacturing value chain as a closed system and feedback loop.

Web Defect Tracking Method

In the present system there may be multiple “subsystems” (printers,steamers, cutters, etc.). Each one of the subsystems can produce adefect in certain instances. For example, one of the printers couldmisprint or the deposited inks could be smudged by coming in physicalcontact with an object. As another example, a steamer that is set toexecute a wrong recipe could produce a segment, of the fabric roll, thatis unusable. In another example, an operator may choose to cut out asegment of the roll fabric and then reconnect it (via stitching). Suchalterations may be recorded and communicated to the designatedsubsystems so that, for example, the cutter is able to accurately andprecisely cutout garment parts from a web and/or so that the mechanicalarms know what parts to expect and where.

As such, a nesting program (e.g., after executing the nesting protocol)may deposit UIDs along the “gutter” region of the fabric roll. The UIDsmay be scannable barcodes, data matrices, or equivalent, and are equallyspaced along the long edges of the fabric. Other UIDs may be used. Forexample, one may create a unique identifier every 5 inches (with anidentical copy of the UID contained within the other, parallel gutter);alternatively, these UIDs may be spaced in any other, regular fashionalong the gutters. The space between each UID (virtual fabric slice) canbe called a “segment” and will contain printed garment parts orgraphical design(s). The nesting program may record the specific garmentparts or graphical design(s) contained within each virtual segment (i.e.between two consecutive UIDs). Note that some of the parts in aparticular segment will not be “whole” since portions of the parts willbe contained in the segment immediately following or preceding thecurrent segment.

As an illustrative example, when a defect is detected one of thefollowing operations may be executed: 1) the operator cuts out thedefective segment of the fabric, scans the two UIDs (one before thedefect region and one immediately after the defect region), andreconnects the fabric. 2) no cutting of the fabric occurs. The operator(or a camera) scans the two UIDs that contain the defective region(which may span multiple segments). Other operations may be executed.The information from the scanned UIDs may be communicated to a computersystem that makes proper adjustments to the main nesting file in orderto exclude the cut segment in later operations (ex. cutting).Additionally or alternatively, the information about the parts (and/orgraphical designs), that were contained within the defective region, maybe stored. The stored information may be further appended with otherdefect data from other rolls of the same fabric material. At the end ofthe production process (or at any time) the stored information may besent back to the nesting software which congregates and re-nests the“missing” parts. Once the parts are nested, the production processfollows (i.e. printing, steaming, cutting, etc.) until all requiredparts are produced. Alternatively or additionally, the same process asabove may be implemented but with UlDs placed among the nested partsinstead of being in the gutters.

The present disclosure comprises at least the following aspects:

Aspect 1: A method for article management, the method comprising:receiving consumer data comprising at least biometric informationassociated with one or more consumers; receiving design inputsindicative of a design of an article, wherein the design of the articleis based on the consumer data; causing output of interactive content toa user interface associated with the one or more consumers, wherein theinteractive content comprises at least a representation of the design ofthe article; and outputting manufacturing data indicative ofinstructions associated with manufacture of the article, wherein theinstructions are based on the design of the article.

Aspect 2: The method of aspect 1, wherein the consumer data furthercomprises consumer preference information.

Aspect 3: The method of aspect 1, further comprising receivingcoloration data indicative of a coloration feasibility, wherein thedesign of the article is dependent on the coloration data.

Aspect 4: The method of aspect 1, wherein the outputting manufacturingdata comprises outputting, to a digital print system, at least a portionof the manufacturing data.

Aspect 5: A method for direct-to-manufacturer article management, themethod comprising: receiving consumer data comprising at least biometricinformation associated with one or more consumers; receiving designinputs indicative of a design of an article, wherein the design of thearticle is based on the consumer data; automatically generating apattern comprising one or more components of the article and one or morecomponents of a second article; and outputting manufacturing dataindicative of instructions associated with manufacture of the article,wherein the instructions are based on the pattern.

Aspect 6: The method of aspect 1, wherein the automatically generating apattern comprises executing nesting optimization.

Aspect 7: A method for article development, the method comprising:receiving consumer data comprising at least biometric informationassociated with one or ore consumers; receiving trend data indicative ofa trend in one or more of article design or article coloration; causingoutput of, based on at least the consumer data and the trend data andvia a user interface, one or more design options; and receiving designinputs indicative of a design of an article.

Aspect 8: The method of aspect 7, wherein the consumer data furthercomprises consumer preference information.

Aspect 9: The method of aspect 7, wherein the one or more design optionscomprise types of fabrics.

Aspect 10: The method of aspect 7, wherein the one or more designoptions are limited based on available fabrics.

Aspect 11: The method of aspect 7, further comprising generating a techpack based on the design of the article and a fabric selection.

Aspect 12: The method of aspect 7, further comprising outputting a billif material based on the design of the fabric.

Aspect 13: A method for color control, the method comprising: receivingdata indicative of one or more characteristics of a substrate for use informing an article; selecting, based on the data indicative of one ormore characteristics of the substrate, a chemical profile or a finishingprocess, or both; and forming at least a portion of the article usingthe selected chemical profile or finishing process, or both.

Aspect 14: The method of aspect 13, wherein the article exhibits a colorthat is within tolerance of a design color.

Aspect 15: A method of color control, the method comprising: performinga first process of a plurality of article management processes to outputa first stage article; capturing, using an inline spectrophotometer,color data associated with the first stage product; comparing the colordata to expected data; and executing a remediation based at least on thecomparing the color data to expected data.

Aspect 16: A method of pretreating textile, the method comprising:receiving a textile; determining a select area of the textile that is tobe printed; causing an applicator to apply a foam chemistry to theselect area of the textile, wherein application of the foam chemistry toareas of the textile outside the select area is minimized; and dryingthe select area of the textile such that a surface of the select area iscapable of being printed.

Aspect 17: A method of pretreating textile, the method comprising:receiving a textile; conditioning, using a plasma, at least a portion ofthe textile to remove one or more contaminants from at least the portionof the textile, and changing one or more of a surface chemistry and atopography of at least the portion of the textile; and applying one ormore chemistries to at least the portion of the textile.

Aspect 18: The method of aspect 17, wherein the conditioning at leastthe portion of the textile further activates a surface of the textile,and wherein activation of the surface of the textile improves aperformance of the one or more chemistries as compared to an unactivatedsurface of the textile with the same one or more chemistries appliedthereto.

Aspect 19: A method for attribution and/or traceability, the methodcomprising disposing one or more unique identifiers on at least aportion of an article, wherein the one or more unique identifiers areinvisible to a human eye and visible with the aid of a predeterminedvision method, and wherein the one or more unique identifiers arereferenced during a manufacturing process comprising digital printing toprovide quality control data for one or more steps in the manufacturingprocess.

Aspect 20: The method of aspect 19, wherein the one or more uniqueidentifiers indicate a registration mechanism for one or more componentsof the article.

Aspect 21: A method for attribution and/or traceability, the methodcomprising disposing one or more unique identifiers on an article,wherein the one or more unique identifiers comprise an invisiblecomponent that is invisible to a human eye and visible with the aid of apredetermined vision method and a visible component that is visible tothe human eye, and wherein the one or more unique identifiers indicateat least attribution data.

Aspect 22: The method of aspect 21, wherein the attribution datacomprises information indicative of the provenance of the article.

Aspect 23: A method for component-level application, the methodcomprising: receiving information indicating locations for applicationof a material on a surface of an article; and selectively disposing,using digital printing or digital extrusion and based on the locations,the material only at the locations, whereby the material is not disposedon at least a portion of the article.

Aspect 24: The method of aspect 23, wherein the material comprises anadhesive.

Aspect 25: The method of aspect 23, wherein the selectively disposing isbased on registration points associated with the article.

Aspect 26: A method for cutting registration, the method comprising:analyzing, using computer vision, a first pattern configuration disposedon printed fabric; implementing a finishing process on the printedfabric, resulting in a second pattern configuration different from thefirst pattern configuration; analyzing, using computer vision, thesecond pattern configuration disposed on the printed fabric;determining, based on the first pattern configuration and the secondpattern configuration, cutting control information; and sending thecutting control information to a cutting system to facilitate cutting ofthe printed fabric.

Aspect 27: A method of cutting comprising: batching one or more customerorders into a batch; nesting a plurality of article components based onthe batch; and cutting the nested components from a substrate such thatone or more tabs connect the cut components to a portion of thesubstrate.

Aspect 28: A method of material handling comprising: arranging a trayand sorting system with an overhead rail system configured for materialhandling; receiving a plurality of material components; sorting, usingthe arranged tray and sorting system and the overhead rail system, theplurality of material components based on one or more of a type ofcomponent or a batched order, or both.

Aspect 29: A system for implementing any one of the methods of aspects1-28.

What is claimed is:
 1. A method for color control, the methodcomprising: receiving article data comprising information associatedwith a consumer order of an article, wherein the article data comprisesat least color information associated with a design color of at least aportion of the article; receiving data indicative of one or morecharacteristics of a substrate for use in forming the article;selecting, based on the article data and the data indicative of one ormore characteristics of the substrate, a chemical profile or a finishingprocess; and forming at least a portion of the article using theselected chemical profile or finishing process, or both, wherein theformed article exhibits a color that is within a predetermined toleranceof the design color.
 2. The method of claim 1, wherein the article datacomprises trend data indicative of a trend in article colorationassociated with the design color of at least a portion of the article.3. The method of claim 1, further comprising receiving coloration dataindicative of a coloration feasibility, wherein a design of the articleis dependent on the coloration data.
 4. The method of claim 1, furthercomprising: performing a first article management process of a pluralityof article management processes to output a first stage articleassociated with the article; capturing, using an inlinespectrophotometer, color data associated with the first stage article;comparing, based on the article data, the color data to the colorinformation, wherein the color information comprises an expected color;and executing a remediation based at least on the comparing the colordata to the expected color.
 5. The method of claim 3, further comprisingdetermining the expected color from a job file.
 6. The method of claim3, further comprising determining a new color to associate with theexpected color.
 7. The method of claim 5, wherein the determining a newcolor to associate with the expected color comprises determining thatthe new paint color needs more or less of a particular color to becloser to the expected color.
 8. The method of claim 5, furthercomprising updating a library of addressable colors with the new color.9. The method of claim 7, further comprising assigning the new color tothe expected color.
 10. The method of claim 7, further comprisingcreating a new project request using the updated library of addressablecolors.
 11. The method of claim 1, further comprising generating a techpack based on the design of the article and a fabric selection oroutputting a bill of material based on the design of the fabric.
 12. Themethod of claim 1, further comprising: receiving a textile, wherein thearticle data further comprises information relating to the textile;determining, based at least on the article data, a select area of thetextile that is to be printed; causing an applicator to apply achemistry to the select area of the textile, wherein application of thechemistry to areas of the textile outside the select area is minimizedand is based on at least the article data; and drying the select area ofthe textile such that a surface of the select area is capable of beingprinted.
 13. The method of claim 12, wherein the chemistry comprises afoam chemistry.
 14. The method of claim 12, further comprising:conditioning, using a plasma, at least the select area of the textile toremove one or more contaminants from at least the select area of thetextile and change one or more of a surface chemistry and a topographyof at least the portion of the textile; and applying one or morechemistries to at least the portion of the textile, wherein one or moreof the conditioning and the applying is based on the article data. 15.The method of claim 14, wherein the conditioning at least the portion ofthe textile further activates a surface of at least the select area ofthe textile, and wherein activation of the surface of the textileimproves a dye color saturation with less dye as compared to anunactivated surface of the textile with the same dye applied thereto.16. A method for color control during a textile printing process, themethod comprising: receiving article data comprising informationassociated with a consumer order of an article to be formed from thetextile, wherein the article data comprises at least color informationassociated with a design color of at least a portion of the article;receiving data indicative of one or more characteristics of a substratefor use in forming the article; selecting, based on the article data andthe data indicative of one or more characteristics of the substrate, achemical profile or a finishing process; performing a first articlemanagement process of a plurality of article management processes tooutput a first stage article associated with the article; capturing,using an inline spectrophotometer, color data associated with the firststage article; comparing, based on the article data, the color data tothe color information, wherein the color information comprises anexpected color; executing a remediation based at least on the comparingthe color data to the expected color; and forming at least a portion ofthe article using the selected chemical profile or finishing process, orboth, wherein the formed article exhibits a color that is within apredetermined tolerance of the design color.
 17. The method of claim 16,further comprising determining a new color to associate with theexpected color.
 18. The method of claim 17, wherein the determining anew color to associate with the expected color comprises determiningthat the new paint color needs more or less of a particular color to becloser to the expected color.
 19. The method of claim 17, furthercomprising updating a library of addressable colors with the new color.20. A system configured to implement the method of claim 1, wherein thesystem comprises one or more of: textile printer, textile laser cutter,textile pick-and-place unit, vision system, computer network withcontrol unit.